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Abstract

Fake or counterfeiting currency, which has been around as long as money has existed, is a major economic problem. Since the US dollar is the most popular form of currency globally, it is the most popular currency to counterfeit. The United States Department of Treasury estimates that between $70 million and $200 million in fake bills are in circulation. The Federal Reserve Bank uses special banknote processing systems to count each bill deposited by the bank and examine them for the possibility of counterfeits. These machines have sensors designed to detect general quality of the bills, including paper type, quality of ink, and color-shifting ink. In this paper, several machine learning algorithms were used to develop an automated identification system for the detection of fake bills. A fake bills dataset, which contains 1500 bill measurements, was used to train several machine learning models. The dataset is split into training and testing sets. The machine learning models are trained with the training set and the accuracy of the models was evaluated with the test set using a 5-fold cross-validation to provide a more reliable measure of the model’s effectiveness. Our initial results are very promising with an accuracy rate of 99% for the best machine learning model. Furthermore, the machine learning model also identifies which bill measurements are critical for the identification of the bill authenticity. These results can provide useful information to the consumers as well as experts to spot fake bills based on bills measurement.

Author Bio

Tianyang Lu is a senior majoring in mathematics at Shandong University in Jinan, Shandong, China. His interests are in mathematical modeling and statistics and its applications. This research was carried out during the six-week summer Global Education, Academics, and Research Skills (GEARS) program in 2023 at North Carolina State University. He is now in the process of applying for graduate studies in statistics.

Hongyang Pang is a senior majoring in mathematics at Nankai University, Tianjin, China. His interests include financial engineering, stochastic process, and graph theory. This research was carried out during the six-week summer Global Education, Academics, and Research Skills (GEARS) program in 2023 at North Carolina State University. He is now in the process of applying for graduate studies in statistics and applied mathematics.

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